Popular Python recipes tagged "meta:requires=matplotlib.pylab"http://code.activestate.com/recipes/langs/python/tags/meta:requires=matplotlib.pylab/2005-05-13T15:58:00-07:00ActiveState Code RecipesMetropolis-Hastings Sampler (Python)
2005-05-13T15:58:00-07:00Flávio Codeço Coelhohttp://code.activestate.com/recipes/users/2434632/http://code.activestate.com/recipes/414200-metropolis-hastings-sampler/
<p style="color: grey">
Python
recipe 414200
by <a href="/recipes/users/2434632/">Flávio Codeço Coelho</a>
(<a href="/recipes/tags/algorithms/">algorithms</a>).
</p>
<p>The Metropolis-Hastings Sampler is the most common Markov-Chain-Monte-Carlo (MCMC) algorithm used to sample from arbitrary probability density functions (PDF). Suppose you want to simulate samples from a random variable which can be described by an arbitrary PDF, i.e., any function which integrates to 1 over a given interval. This algorithm will do just that, as illustrated by the Plot done with Matplotlib. Notice how the samples follow the theoretical PDF.</p>
Gibbs Sampler (Python)
2005-05-11T18:01:57-07:00Flávio Codeço Coelhohttp://code.activestate.com/recipes/users/2434632/http://code.activestate.com/recipes/413086-gibbs-sampler/
<p style="color: grey">
Python
recipe 413086
by <a href="/recipes/users/2434632/">Flávio Codeço Coelho</a>
.
Revision 2.
</p>
<p>The gibbs sampler is an iterative conditional sampler from multidimensional probability density functions (PDFs). The resulting sample is plotted as a scatter plot with the Matplotlib module.</p>